OBJECTIVES: The National Institute of Health and Care Excellence (NICE) makes recommendations on the reimbursement of new medicines based upon their clinical and cost-effectiveness (as defined by their incremental cost per additional Quality Adjusted Life Year [QALY]). Historically, oncology therapies have had lower NICE recommendation rates than those for other therapy areas. The European Medicines Agency is increasingly approving therapies for patients with life-threatening diseases with high unmet needs (encompassing many oncology therapies) at earlier stages of their clinical development, without any supportive comparative Phase 3 data. It may be hypothesized that such oncology therapies would struggle to demonstrate cost-effectiveness in a robust manner and gain NICE approval, which this research investigates.

METHODS: NICE single technology appraisal (STA) guidance for systemic anti-cancer therapies was screened (01/01/2009-31/08/2017) and the recommendation and supportive trial package extracted.

RESULTS: 104 oncology therapies were identified (62% recommended, 16% optimized, 2% only in research, 20% not recommended). Eight therapies were approved on trial packages lacking any comparative Phase 3 data. 75% (6/8) were recommended ), 12.5% (1/8) optimized, and 12.5% (1/8) not recommended). 75% (6/8) were appraised in June 2016 or later. 86% (67) of such recommended therapies were subject to a patient access scheme or managed access agreement.

CONCLUSIONS: NICE have recommended 7/8 oncology STAs supported by a clinical trial package lacking comparative data. The trend for such appraisals is increasing with most having been conducted in the last 18 months. The clinical benchmark of EMA approval for oncology drugs on such a data package seems sufficient to enable acceptance under NICE’s clinical criteria. Nevertheless, NICE’s recommendations are further conditional on cost-effectiveness being adequately demonstrated. Additional price discounts and/or innovative contracting may frequently be required to offset the inherent uncertainties on conducting economic modelling based on such limited clinical data package.